基于多旋翼无人机的机载激光点云树障隐患识别方法

Tree Barrier Hidden Danger Identification Method Using Airborne Laser Point Cloud Based on Multi-Rotor UAV

传统的架空输电线路树障隐患识别方法速度慢,树障隐患识别过程耗时较长。为了解决这个问题,文章设计了一种基于多旋翼无人机的机载激光点云树障隐患识别方法。以激光雷达点云作为识别技术,构建B/S架构的激光雷达点云数据运算框架体系。以多旋翼无人机机体为坐标原点,构建巡检空间坐标系,对树障隐患实施跟踪监测。将不同跟踪周期内的无人机位置点作为不同的空间点,对树障隐患位置进行定位。以无人机飞行过程中3个轴向的不确定参数作为设备隐患距离参数,采用嵌入式容积卡尔曼滤波(Embedded Cubature Kalman Filter,ECKF)算法创建一个随机常数值,控制识别过程的计算量,并实现对树障隐患的识别。实验结果表明,该方法的平均识别程度参数约为82.2%,识别精度数值约为0.94,所需的平均识别时间约为5 ms,证明其满足设计预期。

The traditional tree barrier hidden danger identification method of overhead transmission line is slow and time-consuming.To solve this problem,this paper designs a tree barrier hidden danger identification method using airborne laser point cloud based on multi-rotor UAV.Taking the LIDAR point cloud as the recognition technology,the LIDAR point cloud data operation framework based on B/S architecture is constructed.Taking the multi-rotor UAV body as the coordinate origin,the patrol space coordinate system is constructed to track and monitor the hidden danger of tree barrier.The location points of UAV in different tracking periods are regarded as different space points to locate the hidden danger of tree barriers.The uncertain parameters of three axes in the flight process of UAV are taken as the distance parameters of equipment hidden danger,and the Embedded Cubature Kalman Filter(ECKF)algorithm is used to create a random constant value to control the amount of calculation in the recognition process and realize the recognition of tree barrier hidden danger.The experimental results show that the average recognition degree parameter of the method is about 82.2%,the recognition accuracy value is about 0.94,and the recognition time is about 5ms,which proves that the method meets the design expectation.